Analysis of the distribution pattern of the ectomycorrhizal fungus Cenococcum geophilum under climate change using the optimized MaxEnt model

Abstract Cenococcum geophilum (C. geophilum) is a widely distributed ectomycorrhizal fungus that plays a crucial role in forest ecosystems worldwide. However, the specific ecological factors influencing its global distribution and how climate change will affect its range are still relatively unknown. In this study, we used the MaxEnt model optimized with the kuenm package to simulate changes in the distribution pattern of C. geophilum from the Last Glacial Maximum to the future based on 164 global distribution records and 17 environmental variables and investigated the key environmental factors influencing its distribution. We employed the optimal parameter combination of RM = 4 and FC = QPH, resulting in a highly accurate predictive model. Our study clearly shows that the mean temperature of the coldest quarter and annual precipitation are the key environmental factors influencing the suitable habitats of C. geophilum. Currently, appropriate habitats of C. geophilum are mainly distributed in eastern Asia, west‐central Europe, the western seaboard and eastern regions of North America, and southeastern Australia, covering a total area of approximately 36,578,300 km2 globally. During the Last Glacial Maximum and the mid‐Holocene, C. geophilum had a much smaller distribution area, being mainly concentrated in the Qinling‐Huaihe Line region of China and eastern Peninsular Malaysia. As global warming continues, the future suitable habitat for C. geophilum is projected to shift northward, leading to an expected expansion of the suitable area from 9.21% to 21.02%. This study provides a theoretical foundation for global conservation efforts and biogeographic understanding of C. geophilum, offering new insights into its distribution patterns and evolutionary trends.


| INTRODUC TI ON
The severity of climate extremes is increasing, and recent and projected climate change is exceeding historical trends (Parmesan et al., 2022;Pörtner et al., 2022).This exceeds the potential for species to adapt, necessitating range shifts for many species to survive (Donelson et al., 2023;Scheffers et al., 2016).Many studies suggest that the migratory success of plant species under climate change may depend on their mycorrhizal fungal partners (Bennett & Classen, 2020;Wurzburger et al., 2017).Ectomycorrhizal (ECM) interactions are the dominant mycorrhizal symbiosis for plants in temperate and boreal forests, and there is evidence that the absence of ECM fungi limits plant dispersal (Nuñez et al., 2009;Tedersoo et al., 2012).Therefore, clarifying the dynamics of geographic distribution of ECM fungi under climate change is critical for predicting the impact of climate warming on forest ecosystems globally.
Climate change impacts ECM fungi in diverse ways, making it challenging to predict how it will affect their distributions and activities (Meineke et al., 2018).Species distribution models (SDMs) can predict fungal species distributions and provide a new approach for studying the response of ECM fungi to climate change (Borgelt et al., 2022;Briscoe et al., 2023;Ponti & Sannolo, 2023).These models relate species presence data to climatic and topographic variables, enabling estimates of the past, present, and future distribution of target species (Buonincontri et al., 2023;Chowdhury, 2023;Riva et al., 2023).Among SDMs, the MaxEnt model based on the principle of maximum entropy, has proven most effective in simulating the geographical distribution of species (Phillips et al., 2006;Phillips & Dudík, 2008;West et al., 2016).
The outstanding advantage of the MaxEnt model is its reliable prediction accuracy even with limited sample size and insufficient data (Shi et al., 2023;Zhou et al., 2023).Many studies have demonstrated the reliability of MaxEnt in predicting the potential distribution of fungi (Banasiak et al., 2019;Cogliati et al., 2023;Wei et al., 2021).
ECM fungi play an essential role in nutrient cycling and ecosystem functioning in temperate forest ecosystems (Read et al., 2004).ECM fungi cover the finest root branches of trees and provide nutrients from the soil in exchange for carbon synthesized by the plant (Nehls & Plassard, 2018).In boreal forest soils, ECM fungi contribute up to 39% of microbial biomass (Smith & Read, 2010).Studies have shown that the species richness of ECM fungi follows an unimodal relationship with latitude, peaking between 4000 and 4500 km from the equator (Tedersoo et al., 2012(Tedersoo et al., , 2014)).Additionally, ECM fungi exhibit changes along the elevation gradient (Bahram et al., 2012;Shigyo & Hirao, 2021).These diversity gradients are largely influenced by climatic variables, particularly mean annual temperature and precipitation (Looney et al., 2016).The physical and chemical properties of the soil, such as pH and organic matter content, also strongly influence the ECM fungal community (Ge et al., 2017).Despite these efforts, the relative importance of different ecological factors to ECM fungal communities and how future climate change will affect the distribution of ECM fungi globally remain relatively unknown.
Cenococcum geophilum, a globally distributed ECM fungus, is considered one of the most abundant and ubiquitous species in forest soils and on woody plant roots (Trappe, 1962).C. geophilum occupies a wide range of ecological niches, from tundra to rainforests, and forms symbiotic relationships with over 200 tree species (Dauphin et al., 2021;Obase et al., 2017).Therefore, this fungal group could serve as a model system for studying the biogeography of ECM fungi, and its dynamics under future climate change scenarios may reflect the distribution dynamics of the majority of forest tree species.However, current research on C. geophilum mainly focuses on its genetic phylogeny and environmental tolerance (Li et al., 2022;Obase et al., 2017;Peter et al., 2016)

| Species occurrence data sources
In this study, we collected 203 occurrence records of C. geophilum, including 35 records obtained from fieldwork conducted in China and Japan and 168 records extracted from the Global Biodiversity Information Facility database (GBIF, https://www.gbif.org/).When geographic coordinates were unavailable occurrence data, we used Google Earth to determine latitude and longitude.To address issues of spatial autocorrelation and pseudo-replication of species occurrence data impacting model outputs, we used ENMTools software to ensure that each 5 km 2 grid cell contained only one distribution record (Naimi et al., 2011;Warren et al., 2010).Ultimately, we obtained 164 records representing the effective distribution of C. geophilum (Figure 1).

| Environmental variable
We initially selected 36 environmental parameters that could potentially affect the distribution of C. geophilum in order to model species distribution patterns, including 19 bioclimatic variables, 16 soil variables, and 1 topographic variable (Table S1).We resampled the environmental variables at a spatial resolution of 2.5 min, which is approximately 5 km 2 .Data on bioclimatic variables for different time periods, including the Last Glacial Maximum (LGM), Middle Holocene (MH), current , and future (2050s, 2070s), were obtained from the Worldclim database (http://world clim.org).The topographic variable data were downloaded from the WorldClim database, while the soil variable data were gathered from the Harmonized World Soil Database (HWSD, https:// www.fao.org/soils-porta l/data-hub/soil-class ifica tion/en  'Neill et al., 2016;Riahi et al., 2017).BCC CSM2-MR is one of the most commonly used models for predicting the global climate response to increasing greenhouse gas emissions (Zhang et al., 2018).
Thirty-six environmental variables were inputted into Max-Ent for preliminary modeling to calculate the contribution rates of each environmental factor.Variables with contribution rates below 0.10 were removed.It has been demonstrated that high collinearity among predictor variables can reduce the accuracy of the model's predictions (Zhao, Xiao, et al., 2022).Therefore, we used the EN-MTools software to calculate the Pearson correlation coefficient (r) of the environmental variables (Figure S1) in order to identify highly correlated variables and minimize collinearity among the remaining environmental variables (Warren et al., 2010).For any pair of highly correlated environmental factors (r > .80;Bowen & Stevens, 2020), we retained the variable that had a greater contribution to the growth of C. geophilum.Finally, the model was established using seven climate variables, one topography variable, and nine soil variables (Table S2).

| Modeling procedure and model evaluation
MaxEnt v3.4.1 was used to forecast potentially suitable habitats for C. geophilum based on species occurrence data and environmental variables (Gao et al., 2022;Zhao, Lei, et al., 2022).We randomly selected 70% of the C. geophilum occurrence records as training data and the remaining 30% as testing data (Liu & Shi, 2020).The maximum number of iterations was set at 5000 to ensure sufficient time for the model to converge.
To enhance model performance and prevent overfitting, we optimized Feature Combination (FC) and Regularization Multiplier (RM) using the "kuenm" package in the R software (Cobos et al., 2019;Phillips & Dudík, 2008).A total of 600 candidate models were generated by combining 40 RM settings (0.1-4.0 with intervals of 0.1) and 15 different FC (L, Q, P, H, LQ, LP, LH, QP, QH, PH, LQP, LQH, LPH, QPH, LQPH; where L = linear, Q = quadratic, P = product, H = hinge; Cobos et al., 2019;Phillips et al., 2017).The complexity and goodness-of-fit of these 600 models were evaluated based on a 5% omission rate and the delta from the Akaike information criterion (AICc; Cobos et al., 2019).AICc was used as the performance metric, and the model parameters of the candidate model with the smallest delta AICc were selected for modeling (Liu et al., 2018).Moreover, to reduce potential variation and randomness in the model simulation results, ten replicated rounds of cross-validation were performed, and the final findings are the average of these ten repetitions.
To assess the overall performance of the MaxEnt model simulation results, the threshold-independent analysis of the receiver operating characteristic (ROC) curve ws employed (Gong et al., 2022;Porfirio et al., 2014).However, it has been shown that AUC values are inadequate in evaluating models of the potential distribution of species, so we used TSS (true skill statistics) as a complementary (Ahmadi et al., 2023;Jiménez-Valverde, 2012).

| Potentially suitable area partitions
We  Through optimization, the parameters of the model were changed to RM = 4, FC = QPH, and delta AICc = 0 (Table 1), which indicated the model's optimality.Additionally, the model's omission rate was reduced by 74.9% compared to the preoptimization period.These results showed that the optimized parameter combination significantly reduces the complexity and overfitting of the model.Therefore, RM = 4 and FC = QPH were finally chosen as the parameter settings for 10 replications of the modeling.After testing, the average AUC value of 10 repetitions of modeling was 0.878 (Figure S2) and the average TSS value was 0.959 (Table S3), indicating that the optimized MaxEnt model achieved good results in predicting the potential distribution of C. geophilum.

| Evaluation of environmental factors for the potentially suitable habitats of C. geophilum
The important environmental factors influencing the potentially suitable habitats of C. geophilum were confirmed using the percentage contribution and Jackknife test results.For C. geophilum, mean temperature of the coldest quarter (Bio11, 38.9%) and annual precipitation (Bio12, 24.5%) were the two environmental variables with the highest percentage contribution (Table S2).Two factors, temperature seasonality (Bio4, 8.6%) and topsoil electrical conductivity (T_ECE, 6.7%), also had relatively high percentage contributions (Table S2).The Jackknife test revealed that mean temperature of the coldest quarter (Bio11), annual precipitation (Bio12), temperature seasonality (Bio4), and precipitation of the driest quarter (Bio17, 4%) ranked highest in terms of the regularized training gain when modeled using a single environmental factor (Figure S3).Comprehensive Jackknife test and percentage contribution analysis showed that bio11, bio12, bio4, bio17, and T_ECE were the dominant environ- To further clarify the climatic characteristics of potentially suitable areas for C. geophilum, response curves were plotted to illustrate how the logistic predictions of habitat suitability varied with changes in five important environmental variables (Figure 2).
We found that all five important environmental variables showed a single-peak curve relationship with the probability of the pres-

| Global distribution of suitable habitats for C. geophilum under current climatic conditions
Under current climatic conditions, suitable habitats for C. geophilum in the Northern Hemisphere are concentrated in East Asia, westcentral Europe, the western seaboard, and eastern regions of North America (Figure 3).In the southern hemisphere, the suitable survival zone for C. geophilum is small, mainly concentrated in southeastern Australia (Figure 3).The worldwide area of suitable habitats for C. geophilum is approximately 36,578,300 km 2 , with a highly suitable habitat area of 5,999,700 km 2 (Figure 3).The highly suitable habitats

| Global distribution of suitable habitats for C. geophilum under the paleoclimate climatic conditions
The paleoclimate scenario had much fewer suitable areas for C. geophilum compared with the current conditions (Figure 4).During China, the west coast of North America, and the middle Mississippi River (Figure 4).The overall suitable habitat area for C. geophilum was approximately 630,600 km 2 (Figure 4).From the LGM to the MH, the suitable habitat area declined by 42.05%, while the overall suitable range migrated northward (Figure 4).

| Global distribution of suitable habitats for C. geophilum under future climatic conditions
To investigate the effects of climate change on C. geophilum habitats, we utilized four scenarios (two SSP scenarios for the 2050s and 2070s, respectively) to model habitat suitability (Figure 5).due to its stability and accuracy, previous studies have demonstrated that using default parameters may lead to overfitting and sampling bias, thereby affecting prediction accuracy (Li et al., 2023;Phillips et al., 2006;Shi et al., 2023).Therefore, in this study, we optimized the MaxEnt model using the kuenm package, which identifies the optimal combination of model parameters by iteratively refining the fit between distributional records and environmental variables (Cobos et al., 2019).By adjusting the model parameters and comparing the outcomes, we found that optimizing the RM value from 1 to 4 and the FC value LQPH to QPH reduced the delta AICc of the model from 504.4341 to 0 and therefore the overfitting of the model is greatly reduced (Table 1).The AUC value of the optimized model is 0.878 and the TSS value is 0.959, indicating that the model has a high prediction accuracy (Table S3, Figure S2).The findings reinforce the importance of model optimization, consistent with previous studies (Ma et al., 2023).

| Important environmental factors affecting the distribution of C. geophilum
According to the percent contribution, permutation importance, and   (Lepage et al., 1997).Our study suggests that during the LGM, the center of distribution of C. geophilum was not only in the equatorial region but also in the North Temperate Zone (Figure 4).This finding supports the idea of the tropics as the origin of many ECM fungi, while also indicating that certain regions in the north temperate zone, such as the mountain ranges in southern China, may have provided diverse and stable environmental conditions for ECM fungi during the LGM period.Glacial refuges play a crucial role in the long-term survival and dynamics of living organisms (Tzedakis et al., 2002).The complex topography and heterogeneous environments of the Qinling-Huaihe Line region in China and the thermal stability and slow biological turnover of Peninsular Malaysia East offer significant opportunities for long-term species formation and persistence of ECM fungi (Liu et al., 2022;Wilf & Kooyman, 2023).Both regions are projected to serve as refuges for the northward and southward movements of C. geophilum in response to climatic oscillations during the glacial period.
During the Mid-Holocene period, the suitable survival zone of C. geophilum migrated slightly northward (Figure 4).However, the disappearance of the suitable survival zone in Peninsular Malaysia may be attributed to the tropical temperature exceeding the critical temperature necessary for ECM fungi to survive (Steig, 1999).
Differences in predictions of potential future suitable areas for C. geophilum have been observed across different timeframes and climate scenarios.However, the general trends consistently indicate an expansion of the suitable area for C. geophilum in the future (Figure 6).The expansion is primarily observed in the northern part of the current suitable area, while a contraction is predicted in the southern part (Figure 7).These findings align with previous research indicating that ECM fungi, such as C. geophilum, are likely to migrate toward higher latitudes as the climate continues to warm (Dahl et al., 2023).Taken together, these studies suggest that future climate change aligns with the species characteristics and preferences of C. geophilum regarding temperature and precipitation.Consequently, it is expected that the suitable areas for C. geophilum and ECM fungi will expand to higher latitudes in the future.

| CON CLUS ION
In this study, we used the optimized MaxEnt model to simulate , and there have been no reports on the use of species distribution models to predict the global distribution dynamics of C. geophilum under climate change.We hypothesize that climatic factors are the main drivers of the distribution of C. geophilum at a global scale.As global temperature increases, we predict that low latitudes will reach the limiting temperature suitable for the distribution of C. geophilum in the future.Higher latitudes will provide survival refuges for C. geophilum, and suitable distribution areas are expected to move to higher latitudes.In order to scientifically explore the response of the potential geographical distribution of ECM fungi to climate change in different periods, this study aims to model the geographical distribution of the species C. geophilum using the MaxEnt model.Our study focuses on three key objectives: (1) reconstructing the historical changes in the geographic distribution patterns of C. geophilum since the Last Glacial Maximum period; (2) analyzing the relationship between the predicted potential distribution area of C. geophilum and the main environmental factors; and (3) providing new insights into the conservation and utilization of C. geophilum as well as other ECM fungi.
Geographic locations of Cenococcum geophilum worldwide; (b) the mycorhiza of C. geophilum on the roots of Pinus densiflora; (c) sclerotium of C. geophilum.

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Model accuracy evaluation for simulating the potentially suitable habitats of C. geophilum Based on the 164 distribution points of C. geophilum and 17 environmental variables, we simulated the potential global distribution of C. geophilum at different periods using the MaxEnt model.The default parameters of the model were RM = 1 and FC = LQPH.
mental factors affecting the distribution of C. geophilum under current climatic conditions.The cumulative contribution of these five factors accounted for 82.70% of the total contribution.These findings suggest that climatic factors, particularly Bio11 and Bio12, are key in determining the ecological niche of C. geophilum.In contrast, soil factors have limited influence on its ecological niche, with only T_ECE showing some influence.
for C. geophilum are mainly found in southern and northern regions of China, southern Korea, and Japan in Asia; western England, eastern France, southern Germany, Switzerland, Ireland, the western coast of Norway, central and eastern Iceland, northwestern Spain, northern Portugal, and central Italy in Europe; the central Mississippi River and Gulf of Alaska coast in the eastern United States, the Gulf of Alaska coast in western Canada, and the island of Newfoundland in eastern Canada in North America.
Relationships between key environmental variables and the probability of existence for Cenococcum geophilum. of appropriate habitats existed for C. geophilum, representing only 2.97% of the currently suitable habitats (Figure 4).During this period, the suitable habitats for C. geophilum were concentrated around the vicinity of the Qinling-Huaihe Line region of China, eastern Peninsular Malaysia, and the west coast of North America.During the Mid-Holocene (MH), appropriate habitats for C. geophilum were concentrated in the North of the Qinling-Huaihe Line in

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Potentially suitable areas for Cenococcum geophilum under current climate conditions worldwide.The red areas represent high suitability areas, the green areas represent medium suitability areas, the blue areas represent low suitability areas, and the white areas represent unsuitability areas.F I G U R E 4 Potentially suitable areas for Cenococcum geophilum under paleoclimate change scenarios worldwide during the Last Glacial Maximum (LGM) and Mid Holocene (MH; a: LGM; b: MH).Under the SSP1-2.6 scenario, characterized by the lowest greenhouse gas (GHG) emissions, suitable habitats for C. geophilum expanded by 9.21% in the 2050s and 9.97% in the 2070s compared to the current time (Figure6).This expansion trend intensified in the SSP5-8.5 scenario, representing the highest GHG emissions, with a 15.10% increase in suitable habitats for C. geophilum in the 2050s and a 21.02% increase in the 2070s compared to the current period (Figure6).Therefore, the area of suitable habitat for C. geophilum increases with time and the extent of greenhouse gas emissions.Comparative analyses of changes in spatial patterns of C. geophilum under two different future climate scenarios suggest that climate change will lead to an expansion of habitat for C. geophilum, and that this expansion will occur mainly in the northern regions, particularly in Russia (north of Kazakhstan, the eastern coast), southern and eastern Canada, northwestern and central Alaska, and the United Accuracy of MaxEnt after optimization In this study, we employed the optimized MaxEnt model to analyze the global distribution of C. geophilum over time within the context of global climate change.Although the MaxEnt model is widely used for simulating species' potential distribution under climate change the jackknife test of the MaxEnt model, we have concluded that the mean temperature of the coldest quarter (−17.92 to 17.42°C), annual precipitation (≥280.73mm), temperature seasonality (188.54%-1548.8%),precipitation of the driest quarter (≥4.87 mm), and topsoil F I G U R E 5 Potentially suitable areas for Cenococcum geophilum under future change scenarios worldwide in the 2050s and 2070s (a: 2050 SSP 1-2.6; b: 2070 SSP 1-2.6; c: 2050 SSP 5-8.5; d: 2070 SSP 5-8.5).F I G U R E 6 Areas with varying potential suitability for Cenococcum geophilum relative to the current situation under different climate scenarios worldwide (a: SSP 1-2.6; b: SSP 5-8.5).F I G U R E 7 Future area changes in different suitable areas for Cenococcum geophilum relative to current climate change scenarios (a: 2050 SSP 1-2.6; b: 2070 SSP 1-2.6; c: 2050 SSP 5-8.5; d: 2070 SSP 5-8.5).The red color represents expansion areas, green represents contraction areas, blue represents unchanged areas, and white represents unsuitable areas.electrical conductivity (≥0.034 dS/m) are the dominant factors restricting the current distribution of C. geophilum (Figure 2).
Furthermore, the expanded suitable areas for C. geophilum are primarily concentrated in the overlapping region of the north temperate and cold zones.It is, thus, believed that the increased temperature and precipitation in this area create favorable conditions for its survival and growth.Therefore, this region becomes particularly sensitive for C. geophilum in terms of adapting to climate change.To effectively conserve and manage ECM fungal responses to climate change, it is crucial to prioritize these habitats and develop appropriate conservation strategies.Additionally, under the same representative concentration pathway, the magnitude of change in the suitable area for C. geophilum is positively correlated with time.Moreover, the range of changes becomes more significant as the representative concentration pathway intensifies.This further indicates the sensitivity of ECM fungi to the impacts of climate change.
changes in the distribution pattern of C. geophilum from the Last Glacial Maximum into the future and explored the key environmental factors affecting its distribution.The results indicate that temperature and precipitation are important factors influencing the global

| Response of the spatial distribution pattern of C. geophilum to climate change
(Obase et al., 2016)est that C. geophilum is better suited to areas that are cooler, wetter, and have four distinct seasons.These findings are consistent with previous studies indicating that C. geophilum dominates temperate to boreal forest systems(Obase et al., 2016).